The subject matter described herein relates to a prevention of invalid selections based on machine learning of user specific latency. Search engines provide a powerful tool for locating resources in large data repositories, e.g., resources on the Internet or resources stored on computer networks. These resources can be located in response to a search query submitted by a user.
In one approach to entering queries, the user enters a query by adding successive search terms until all search terms are entered. Once the user signals that all of the search terms of the query are entered, the query is sent to a search engine. The user may have alternative ways of signaling completion of the query by, for example, entering a return character by pressing the enter key on a keyboard, or by clicking on a search button on a screen with a finger gesture. Once the query is received, the search engine processes the search query, searches for resources responsive to the search query, and returns resources to a client device of the user.
In another approach, a search system can monitor the input of a search query by a user. Before the user finishes entering the search query, the search system can create a set of suggested queries. The suggested queries can be sent to a client device for possible selection by the user. If one of the suggested queries is selected, the search engine processes the selected search query, searches for resources responsive to the selected search query, and returns resources to a client device of the user. In another approach, the search engine can process suggested search queries without receiving any selection input. In this approach, the search engine can process the suggested query based on a high confidence of a guess performed by the search system.